Overview

Dataset statistics

Number of variables13
Number of observations4335
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory440.4 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

monetary_purchase is highly correlated with quant_invoices and 5 other fieldsHigh correlation
monetary_returns is highly correlated with quant_items and 1 other fieldsHigh correlation
recency_days is highly correlated with avg_recency_dayHigh correlation
quant_invoices is highly correlated with monetary_purchase and 3 other fieldsHigh correlation
quant_items is highly correlated with monetary_purchase and 6 other fieldsHigh correlation
quant_prod_uniq is highly correlated with monetary_purchase and 2 other fieldsHigh correlation
avg_ticket is highly correlated with monetary_purchase and 4 other fieldsHigh correlation
avg_recency_day is highly correlated with recency_days and 1 other fieldsHigh correlation
freq_purchases is highly correlated with quant_invoices and 1 other fieldsHigh correlation
quant_returns is highly correlated with monetary_purchase and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with monetary_purchase and 3 other fieldsHigh correlation
avg_uniq_basket_size is highly correlated with quant_prod_uniq and 1 other fieldsHigh correlation
monetary_returns is highly skewed (γ1 = -56.85806933) Skewed
avg_ticket is highly skewed (γ1 = 59.5817417) Skewed
freq_purchases is highly skewed (γ1 = 59.06192797) Skewed
quant_returns is highly skewed (γ1 = 62.48337706) Skewed
avg_basket_size is highly skewed (γ1 = 48.41859348) Skewed
customer_id has unique values Unique
monetary_returns has 2795 (64.5%) zeros Zeros
freq_purchases has 1554 (35.8%) zeros Zeros
quant_returns has 2795 (64.5%) zeros Zeros

Reproduction

Analysis started2022-12-19 15:33:27.306581
Analysis finished2022-12-19 15:33:47.850768
Duration20.54 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct4335
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.11995
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:47.937776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12615.7
Q113813.5
median15300
Q316779.5
95-th percentile17984.3
Maximum18287
Range5940
Interquartile range (IQR)2966

Descriptive statistics

Standard deviation1721.77243
Coefficient of variation (CV)0.1125259089
Kurtosis-1.195948071
Mean15301.11995
Median Absolute Deviation (MAD)1484
Skewness0.0009307401652
Sum66330355
Variance2964500.301
MonotonicityNot monotonic
2022-12-19T12:33:48.048012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
174441
 
< 0.1%
159091
 
< 0.1%
136181
 
< 0.1%
160501
 
< 0.1%
178791
 
< 0.1%
175621
 
< 0.1%
153341
 
< 0.1%
124501
 
< 0.1%
135681
 
< 0.1%
Other values (4325)4325
99.8%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123491
< 0.1%
123501
< 0.1%
123521
< 0.1%
123531
< 0.1%
123541
< 0.1%
123551
< 0.1%
123561
< 0.1%
123571
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182811
< 0.1%
182801
< 0.1%
182781
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%

monetary_purchase
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4248
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1951.137476
Minimum3.75
Maximum259657.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:48.163969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile112.164
Q1305.685
median668.04
Q31640.115
95-th percentile5744.689
Maximum259657.3
Range259653.55
Interquartile range (IQR)1334.43

Descriptive statistics

Standard deviation7790.321213
Coefficient of variation (CV)3.992707488
Kurtosis469.1914378
Mean1951.137476
Median Absolute Deviation (MAD)463.8
Skewness18.76917418
Sum8458180.96
Variance60689104.6
MonotonicityNot monotonic
2022-12-19T12:33:48.282432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.324
 
0.1%
35.43
 
0.1%
79.23
 
0.1%
4403
 
0.1%
113.53
 
0.1%
363.653
 
0.1%
1202
 
< 0.1%
324.242
 
< 0.1%
116.012
 
< 0.1%
110.382
 
< 0.1%
Other values (4238)4308
99.4%
ValueCountFrequency (%)
3.751
< 0.1%
6.21
< 0.1%
6.91
< 0.1%
12.751
< 0.1%
13.31
< 0.1%
152
< 0.1%
171
< 0.1%
20.82
< 0.1%
21.951
< 0.1%
25.52
< 0.1%
ValueCountFrequency (%)
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
139638.061
< 0.1%
124564.531
< 0.1%
116725.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%
65019.621
< 0.1%

monetary_returns
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1121
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-115.996203
Minimum-168469.6
Maximum0
Zeros2795
Zeros (%)64.5%
Negative1540
Negative (%)35.5%
Memory size34.0 KiB
2022-12-19T12:33:48.400403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-168469.6
5-th percentile-175.201
Q1-15.3
median0
Q30
95-th percentile0
Maximum0
Range168469.6
Interquartile range (IQR)15.3

Descriptive statistics

Standard deviation2702.109098
Coefficient of variation (CV)-23.29480645
Kurtosis3491.260968
Mean-115.996203
Median Absolute Deviation (MAD)0
Skewness-56.85806933
Sum-502843.54
Variance7301393.578
MonotonicityNot monotonic
2022-12-19T12:33:48.512064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02795
64.5%
-12.7521
 
0.5%
-4.9518
 
0.4%
-9.9517
 
0.4%
-1516
 
0.4%
-5.912
 
0.3%
-25.511
 
0.3%
-4.2510
 
0.2%
-3.758
 
0.2%
-19.98
 
0.2%
Other values (1111)1419
32.7%
ValueCountFrequency (%)
-168469.61
< 0.1%
-392671
< 0.1%
-22998.41
< 0.1%
-21889.481
< 0.1%
-12158.91
< 0.1%
-11202.441
< 0.1%
-8593.151
< 0.1%
-8495.011
< 0.1%
-6130.221
< 0.1%
-5759.641
< 0.1%
ValueCountFrequency (%)
02795
64.5%
-0.422
 
< 0.1%
-0.651
 
< 0.1%
-0.771
 
< 0.1%
-0.951
 
< 0.1%
-1.254
 
0.1%
-1.454
 
0.1%
-1.641
 
< 0.1%
-1.655
 
0.1%
-1.72
 
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct304
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.05167243
Minimum0
Maximum373
Zeros35
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:48.746913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q117
median50
Q3142
95-th percentile311
Maximum373
Range373
Interquartile range (IQR)125

Descriptive statistics

Standard deviation99.93607143
Coefficient of variation (CV)1.085651882
Kurtosis0.4281095158
Mean92.05167243
Median Absolute Deviation (MAD)40
Skewness1.24470537
Sum399044
Variance9987.218372
MonotonicityNot monotonic
2022-12-19T12:33:48.858104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102
 
2.4%
494
 
2.2%
394
 
2.2%
289
 
2.1%
879
 
1.8%
1077
 
1.8%
1774
 
1.7%
772
 
1.7%
970
 
1.6%
2264
 
1.5%
Other values (294)3520
81.2%
ValueCountFrequency (%)
035
 
0.8%
1102
2.4%
289
2.1%
394
2.2%
494
2.2%
548
1.1%
772
1.7%
879
1.8%
970
1.6%
1077
1.8%
ValueCountFrequency (%)
37317
0.4%
37217
0.4%
3716
 
0.1%
3693
 
0.1%
3685
 
0.1%
3675
 
0.1%
36610
0.2%
36510
0.2%
3646
 
0.1%
3626
 
0.1%

quant_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct57
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.242675894
Minimum1
Maximum209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:48.978521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile13
Maximum209
Range208
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.605354551
Coefficient of variation (CV)1.792584383
Kurtosis257.8028706
Mean4.242675894
Median Absolute Deviation (MAD)1
Skewness12.25879931
Sum18392
Variance57.84141785
MonotonicityNot monotonic
2022-12-19T12:33:49.089022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11500
34.6%
2833
19.2%
3503
 
11.6%
4394
 
9.1%
5238
 
5.5%
6172
 
4.0%
7141
 
3.3%
898
 
2.3%
968
 
1.6%
1154
 
1.2%
Other values (47)334
 
7.7%
ValueCountFrequency (%)
11500
34.6%
2833
19.2%
3503
 
11.6%
4394
 
9.1%
5238
 
5.5%
6172
 
4.0%
7141
 
3.3%
898
 
2.3%
968
 
1.6%
1053
 
1.2%
ValueCountFrequency (%)
2091
< 0.1%
2001
< 0.1%
1241
< 0.1%
971
< 0.1%
912
< 0.1%
861
< 0.1%
622
< 0.1%
601
< 0.1%
571
< 0.1%
551
< 0.1%

quant_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1757
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1126.843829
Minimum1
Maximum80997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:49.206921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile46
Q1160
median378
Q3988
95-th percentile3535.5
Maximum80997
Range80996
Interquartile range (IQR)828

Descriptive statistics

Standard deviation3919.569049
Coefficient of variation (CV)3.478360485
Kurtosis213.9149559
Mean1126.843829
Median Absolute Deviation (MAD)275
Skewness13.22510225
Sum4884868
Variance15363021.53
MonotonicityNot monotonic
2022-12-19T12:33:49.324126image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8820
 
0.5%
12017
 
0.4%
8416
 
0.4%
14416
 
0.4%
12815
 
0.3%
14615
 
0.3%
7215
 
0.3%
10614
 
0.3%
14014
 
0.3%
15014
 
0.3%
Other values (1747)4179
96.4%
ValueCountFrequency (%)
15
0.1%
25
0.1%
34
0.1%
47
0.2%
53
0.1%
63
0.1%
71
 
< 0.1%
81
 
< 0.1%
92
 
< 0.1%
105
0.1%
ValueCountFrequency (%)
809971
< 0.1%
798811
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577721
< 0.1%
502551
< 0.1%

quant_prod_uniq
Real number (ℝ≥0)

HIGH CORRELATION

Distinct463
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.00207612
Minimum1
Maximum7846
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:49.448215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q117
median41
Q3100
95-th percentile314
Maximum7846
Range7845
Interquartile range (IQR)83

Descriptive statistics

Standard deviation226.1535221
Coefficient of variation (CV)2.485146842
Kurtosis499.4270294
Mean91.00207612
Median Absolute Deviation (MAD)30
Skewness18.41531394
Sum394494
Variance51145.41555
MonotonicityNot monotonic
2022-12-19T12:33:49.565820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1083
 
1.9%
676
 
1.8%
976
 
1.8%
173
 
1.7%
1572
 
1.7%
1170
 
1.6%
2868
 
1.6%
867
 
1.5%
767
 
1.5%
567
 
1.5%
Other values (453)3616
83.4%
ValueCountFrequency (%)
173
1.7%
252
1.2%
357
1.3%
449
1.1%
567
1.5%
676
1.8%
767
1.5%
867
1.5%
976
1.8%
1083
1.9%
ValueCountFrequency (%)
78461
< 0.1%
55881
< 0.1%
50951
< 0.1%
45911
< 0.1%
26981
< 0.1%
23791
< 0.1%
18181
< 0.1%
16761
< 0.1%
16361
< 0.1%
15021
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct4292
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.57841831
Minimum2.101285714
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:49.686489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.101285714
5-th percentile4.525895261
Q112.29895425
median17.58438596
Q324.67598759
95-th percentile93.53833333
Maximum56157.5
Range56155.39871
Interquartile range (IQR)12.37703334

Descriptive statistics

Standard deviation885.3992073
Coefficient of variation (CV)17.50547441
Kurtosis3734.691061
Mean50.57841831
Median Absolute Deviation (MAD)6.234912281
Skewness59.5817417
Sum219257.4434
Variance783931.7562
MonotonicityNot monotonic
2022-12-19T12:33:49.791313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.54
 
0.1%
154
 
0.1%
1794
 
0.1%
76.324
 
0.1%
18.73
 
0.1%
24.42
 
< 0.1%
172
 
< 0.1%
207.52
 
< 0.1%
20.362
 
< 0.1%
19.3752
 
< 0.1%
Other values (4282)4306
99.3%
ValueCountFrequency (%)
2.1012857141
< 0.1%
2.1505882351
< 0.1%
2.2411
< 0.1%
2.2643751
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5048760331
< 0.1%
2.508371561
< 0.1%
2.547049181
< 0.1%
2.5639583331
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
13305.51
< 0.1%
4453.431
< 0.1%
38611
< 0.1%
30961
< 0.1%
2033.11
< 0.1%
2027.861
< 0.1%
1687.21
< 0.1%
1377.0777781
< 0.1%
1001.21
< 0.1%

avg_recency_day
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1429
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.5766236
Minimum5.687022901
Maximum373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:49.905504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum5.687022901
5-th percentile56.71678322
Q1134.1
median218.3333333
Q3373
95-th percentile373
Maximum373
Range367.3129771
Interquartile range (IQR)238.9

Descriptive statistics

Standard deviation118.1998573
Coefficient of variation (CV)0.497523096
Kurtosis-1.47991667
Mean237.5766236
Median Absolute Deviation (MAD)126.6190476
Skewness-0.08400976914
Sum1029894.663
Variance13971.20626
MonotonicityNot monotonic
2022-12-19T12:33:50.019564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3731554
35.8%
18715
 
0.3%
20215
 
0.3%
19913
 
0.3%
23212
 
0.3%
19712
 
0.3%
193.511
 
0.3%
19210
 
0.2%
19510
 
0.2%
20410
 
0.2%
Other values (1419)2673
61.7%
ValueCountFrequency (%)
5.6870229011
< 0.1%
6.6017699121
< 0.1%
6.6517857141
< 0.1%
8.2888888891
< 0.1%
8.3707865171
< 0.1%
10.450704231
< 0.1%
11.212121211
< 0.1%
13.70370371
< 0.1%
14.037735851
< 0.1%
15.104166671
< 0.1%
ValueCountFrequency (%)
3731554
35.8%
369.51
 
< 0.1%
3691
 
< 0.1%
368.51
 
< 0.1%
3681
 
< 0.1%
3652
 
< 0.1%
364.51
 
< 0.1%
3642
 
< 0.1%
362.51
 
< 0.1%
3622
 
< 0.1%

freq_purchases
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1232
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03975532161
Minimum0
Maximum34
Zeros1554
Zeros (%)35.8%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:50.140950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01492537313
Q30.0303030303
95-th percentile0.09030590263
Maximum34
Range34
Interquartile range (IQR)0.0303030303

Descriptive statistics

Standard deviation0.5364518284
Coefficient of variation (CV)13.49383697
Kurtosis3712.338665
Mean0.03975532161
Median Absolute Deviation (MAD)0.01492537313
Skewness59.06192797
Sum172.3393192
Variance0.2877805642
MonotonicityNot monotonic
2022-12-19T12:33:50.251095image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01554
35.8%
0.0714285714315
 
0.3%
0.0285714285714
 
0.3%
0.0158730158714
 
0.3%
0.0476190476214
 
0.3%
0.030303030313
 
0.3%
0.0238095238113
 
0.3%
0.0645161290313
 
0.3%
0.02512
 
0.3%
0.0384615384612
 
0.3%
Other values (1222)2661
61.4%
ValueCountFrequency (%)
01554
35.8%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0056022408962
 
< 0.1%
0.0056179775281
 
< 0.1%
0.0056338028172
 
< 0.1%
0.0056818181821
 
< 0.1%
0.0056980056982
 
< 0.1%
ValueCountFrequency (%)
341
 
< 0.1%
61
 
< 0.1%
41
 
< 0.1%
26
0.1%
1.51
 
< 0.1%
1.3333333332
 
< 0.1%
15
0.1%
0.66666666673
0.1%
0.56032171581
 
< 0.1%
0.53763440861
 
< 0.1%

quant_returns
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct216
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.3893887
Minimum0
Maximum80995
Zeros2795
Zeros (%)64.5%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:50.369291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile60
Maximum80995
Range80995
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1252.611612
Coefficient of variation (CV)28.86907719
Kurtosis4028.482313
Mean43.3893887
Median Absolute Deviation (MAD)0
Skewness62.48337706
Sum188093
Variance1569035.85
MonotonicityNot monotonic
2022-12-19T12:33:50.483310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02795
64.5%
1186
 
4.3%
2156
 
3.6%
3107
 
2.5%
486
 
2.0%
676
 
1.8%
564
 
1.5%
1250
 
1.2%
846
 
1.1%
746
 
1.1%
Other values (206)723
 
16.7%
ValueCountFrequency (%)
02795
64.5%
1186
 
4.3%
2156
 
3.6%
3107
 
2.5%
486
 
2.0%
564
 
1.5%
676
 
1.8%
746
 
1.1%
846
 
1.1%
941
 
0.9%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
80591
< 0.1%
46271
< 0.1%
37681
< 0.1%
33341
< 0.1%
29751
< 0.1%
20221
< 0.1%
20121
< 0.1%
19201
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2122
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.447602
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:50.605154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q193
median161.75
Q3271
95-th percentile597.2375
Maximum40498.5
Range40497.5
Interquartile range (IQR)178

Descriptive statistics

Standard deviation681.4721968
Coefficient of variation (CV)2.882127757
Kurtosis2818.432136
Mean236.447602
Median Absolute Deviation (MAD)81.25
Skewness48.41859348
Sum1025000.355
Variance464404.355
MonotonicityNot monotonic
2022-12-19T12:33:50.840857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10019
 
0.4%
8218
 
0.4%
8818
 
0.4%
12018
 
0.4%
7317
 
0.4%
14416
 
0.4%
7216
 
0.4%
6016
 
0.4%
13616
 
0.4%
10616
 
0.4%
Other values (2112)4165
96.1%
ValueCountFrequency (%)
16
0.1%
1.51
 
< 0.1%
24
0.1%
33
0.1%
3.3333333331
 
< 0.1%
47
0.2%
53
0.1%
5.3333333331
 
< 0.1%
5.6666666671
 
< 0.1%
63
0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
78241
< 0.1%
6009.3333331
< 0.1%
43001
< 0.1%
42821
< 0.1%
42801
< 0.1%
3868.651
< 0.1%
30281
< 0.1%
29241
< 0.1%
28801
< 0.1%

avg_uniq_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct933
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.73424121
Minimum0.2
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.0 KiB
2022-12-19T12:33:50.965826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q18
median14
Q324
95-th percentile52
Maximum219
Range218.8
Interquartile range (IQR)16

Descriptive statistics

Standard deviation17.22847967
Coefficient of variation (CV)0.9196251655
Kurtosis14.94353183
Mean18.73424121
Median Absolute Deviation (MAD)7.4
Skewness2.817387236
Sum81212.93565
Variance296.8205118
MonotonicityNot monotonic
2022-12-19T12:33:51.077183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195
 
2.2%
990
 
2.1%
889
 
2.1%
1088
 
2.0%
1386
 
2.0%
785
 
2.0%
681
 
1.9%
1178
 
1.8%
1475
 
1.7%
575
 
1.7%
Other values (923)3493
80.6%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.253
 
0.1%
0.33333333337
0.2%
0.41
 
< 0.1%
0.40909090911
 
< 0.1%
0.512
0.3%
0.54545454551
 
< 0.1%
0.57142857141
 
< 0.1%
0.61
 
< 0.1%
0.61764705881
 
< 0.1%
ValueCountFrequency (%)
2191
< 0.1%
1771
< 0.1%
1711
< 0.1%
1551
< 0.1%
1541
< 0.1%
1482
< 0.1%
1411
< 0.1%
1311
< 0.1%
1281
< 0.1%
1271
< 0.1%

Interactions

2022-12-19T12:33:46.207902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:28.716279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.057428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.585446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.030954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.546879image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.889007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.426233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.869168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.343022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.722206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.234035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.690446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.305433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:28.829874image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.161300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.689757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.133733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.643103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.991415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.532553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.976134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.446401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.831960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.338717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.797582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.399287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:28.930219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.261310image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.798107image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.236363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.745991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.095263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.636006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.083957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.550505image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.942507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.452074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.901080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.504149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.037612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.369611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.915700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.344826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.860281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.206886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.750534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.189765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.661471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.049966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.569731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.012511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.605557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.140174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.474434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.025312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.453636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.967353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.315115image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.861620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.293425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.770394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.152692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.684555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.119497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.697661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.234397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.575194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.126889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.554554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.061026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.415250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.963715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.388736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.868636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.247169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.794255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.219190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.799835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.340748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.812795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.242329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.668431image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.165174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.527709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.076812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.496948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.980628image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.364679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.909313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.331244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.903670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.446820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:30.922732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.364449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:33.898354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.270636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.644333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.194220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.603280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.089432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.484780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.020738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.442456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.998998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.543790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.021396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.473202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.000391image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.368228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.750607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.302960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.703664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.190174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.582780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.125774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.546936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:47.099583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.645742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.127048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.586186image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.105780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.468477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:36.990135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.423495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:39.807678image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.296636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.684513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.234750image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.655520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:47.201467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.751030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.233314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.694282image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.217184image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.570510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.098989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.544088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.031593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.404116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:42.795744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.346658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.765423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:47.306434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.855816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.346620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.806986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.327798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.685331image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.211369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.654603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.139226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.514241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.035800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.457995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:45.878182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:47.412625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:29.963259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:31.468585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:32.925098image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:34.443007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:35.796902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:37.323261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:38.764752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:40.246806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:41.623995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:43.140088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:44.580340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-19T12:33:46.111803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-19T12:33:51.180743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-12-19T12:33:51.330915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-19T12:33:51.487904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-19T12:33:51.645156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-19T12:33:51.803047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-19T12:33:47.562943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-19T12:33:47.765968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

customer_idmonetary_purchasemonetary_returnsrecency_daysquant_invoicesquant_itemsquant_prod_uniqavg_ticketavg_recency_dayfreq_purchasesquant_returnsavg_basket_sizeavg_uniq_basket_size
0178505391.21-102.58372.034.01733.0297.018.152222187.00000034.00000040.050.9705880.617647
1130473237.54-158.4431.010.01391.0172.018.82290789.3750000.02924036.0139.10000010.600000
2125836705.38-76.042.015.05028.0232.028.90250049.6000000.04043150.0335.2000007.600000
313748948.250.0095.05.0439.028.033.866071162.7500000.0179860.087.8000004.800000
415100876.00-240.90333.03.080.03.0292.000000137.6666670.07500022.026.6666670.333333
5152914668.30-71.7925.015.02103.0103.045.32330148.0666670.04310329.0140.2000004.133333
6146885630.87-523.497.021.03621.0327.017.21978636.9500000.057377399.0172.4285717.047619
7178095411.91-784.2916.012.02057.061.088.71983673.0000000.03361342.0171.4166673.833333
81531160767.90-1348.560.091.038194.02379.025.5434648.2888890.243968474.0419.7142866.230769
9145278508.82-797.442.055.02089.0972.08.75393013.7037040.14986440.037.9818186.000000

Last rows

customer_idmonetary_purchasemonetary_returnsrecency_daysquant_invoicesquant_itemsquant_prod_uniqavg_ticketavg_recency_dayfreq_purchasesquant_returnsavg_basket_sizeavg_uniq_basket_size
43251600012393.700.002.03.05110.09.01377.077778373.00.00.01703.3333333.0
4326151953861.000.002.01.01404.01.03861.000000373.00.00.01404.0000001.0
432714087194.42-12.752.01.0251.069.02.817681373.00.01.0251.00000061.0
432814204161.030.002.01.082.044.03.659773373.00.00.082.00000036.0
432915471469.480.002.01.0266.077.06.097143373.00.00.0266.00000067.0
433013436196.890.001.01.076.012.016.407500373.00.00.076.00000012.0
433115520343.500.001.01.0314.018.019.083333373.00.00.0314.00000018.0
433213298360.000.001.01.096.02.0180.000000373.00.00.096.0000002.0
433314569227.390.001.01.079.012.018.949167373.00.00.079.00000010.0
433412713794.550.000.01.0505.037.021.474324373.00.00.0505.00000037.0